Root Mean Squared Error

Root Mean Squared Error (RMSE) is a widely used metric for measuring the accuracy of a model's predictions. It quantifies the difference between predicted values and actual values, providing a clear indication of how well a model performs. RMSE is calculated by taking the square root of the average of the squared differences between predicted and observed values. This metric is particularly sensitive to outliers, making it a robust choice for evaluating models in various applications, such as regression analysis and machine learning. Common use cases include assessing predictive models in finance, healthcare, and engineering, where accuracy is critical.